From 16d4e0054986cd5036cc311cc45fa6dff36cc9da Mon Sep 17 00:00:00 2001
From: 北念 <lzr265946@alibaba-inc.com>
Date: 星期四, 09 二月 2023 17:53:04 +0800
Subject: [PATCH] add BiCifParaformer
---
funasr/utils/timestamp_tools.py | 58 ++++++++++++++++++++++++++++++++++++++++++++++++----------
1 files changed, 48 insertions(+), 10 deletions(-)
diff --git a/funasr/utils/timestamp_tools.py b/funasr/utils/timestamp_tools.py
index 3afaa40..12337d1 100644
--- a/funasr/utils/timestamp_tools.py
+++ b/funasr/utils/timestamp_tools.py
@@ -86,14 +86,52 @@
else:
return time_stamp_list
-
-def time_stamp_lfr6_advance(tst: List, text: str):
- # advanced timestamp prediction for BiCIF_Paraformer using upsampled alphas
- ds_alphas, ds_cif_peak, us_alphas, us_cif_peak = tst
- if text.endswith('</s>'):
- text = text[:-4]
+def time_stamp_lfr6_pl(us_alphas, us_cif_peak, char_list, begin_time=0.0, end_time=None):
+ START_END_THRESHOLD = 5
+ TIME_RATE = 10.0 * 6 / 1000 / 3 # 3 times upsampled
+ if len(us_alphas.shape) == 3:
+ alphas, cif_peak = us_alphas[0], us_cif_peak[0] # support inference batch_size=1 only
else:
- text = text[:-1]
- logging.warning("found text does not end with </s>")
- assert int(ds_alphas.sum() + 1e-4) - 1 == len(text)
-
+ alphas, cif_peak = us_alphas, us_cif_peak
+ num_frames = cif_peak.shape[0]
+ if char_list[-1] == '</s>':
+ char_list = char_list[:-1]
+ # char_list = [i for i in text]
+ timestamp_list = []
+ # for bicif model trained with large data, cif2 actually fires when a character starts
+ # so treat the frames between two peaks as the duration of the former token
+ fire_place = torch.where(cif_peak>1.0-1e-4)[0].cpu().numpy() - 1.5
+ num_peak = len(fire_place)
+ assert num_peak == len(char_list) + 1 # number of peaks is supposed to be number of tokens + 1
+ # begin silence
+ if fire_place[0] > START_END_THRESHOLD:
+ char_list.insert(0, '<sil>')
+ timestamp_list.append([0.0, fire_place[0]*TIME_RATE])
+ # tokens timestamp
+ for i in range(len(fire_place)-1):
+ # the peak is always a little ahead of the start time
+ # timestamp_list.append([(fire_place[i]-1.2)*TIME_RATE, fire_place[i+1]*TIME_RATE])
+ timestamp_list.append([(fire_place[i])*TIME_RATE, fire_place[i+1]*TIME_RATE])
+ # cut the duration to token and sil of the 0-weight frames last long
+ # tail token and end silence
+ if num_frames - fire_place[-1] > START_END_THRESHOLD:
+ _end = (num_frames + fire_place[-1]) / 2
+ timestamp_list[-1][1] = _end*TIME_RATE
+ timestamp_list.append([_end*TIME_RATE, num_frames*TIME_RATE])
+ char_list.append("<sil>")
+ else:
+ timestamp_list[-1][1] = num_frames*TIME_RATE
+ if begin_time: # add offset time in model with vad
+ for i in range(len(timestamp_list)):
+ timestamp_list[i][0] = timestamp_list[i][0] + begin_time / 1000.0
+ timestamp_list[i][1] = timestamp_list[i][1] + begin_time / 1000.0
+ res_txt = ""
+ for char, timestamp in zip(char_list, timestamp_list):
+ res_txt += "{} {} {};".format(char, timestamp[0], timestamp[1])
+ logging.warning(res_txt) # for test
+ res = []
+ for char, timestamp in zip(char_list, timestamp_list):
+ if char != '<sil>':
+ res.append([int(timestamp[0] * 1000), int(timestamp[1] * 1000)])
+ return res
+
--
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